2016 CVPR CVPR 2016

Theory and Practice of Structure-From-Motion Using Affine Correspondences

Abstract

Affine Correspondences (ACs) are more informative than Point Correspondences (PCs) that are used as input in mainstream algorithms for Structure-from-Motion (SfM). Since ACs enable to estimate models from fewer correspondences, its use can dramatically reduce the number of combinations during the iterative step of sample-and-test that exists in most SfM pipelines. However, using ACs instead of PCs as input for SfM passes by fully understanding the relations between ACs and multi-view geometry, as well as by establishing practical, effective AC-based algorithms. This article is a step forward into this direction, by providing a clear account about how ACs constrain the two-view geometry, and by proposing new algorithms for plane segmentation and visual odometry that compare favourably with respect to methods relying in PCs.

🌉 Interdisciplinary Bridge — Computer Vision and Machine Learning
🧭 Keyword Pioneer — affine correspondence
🐣 Hot Topic Early Bird — camera pose estimation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio